Genetic studies have identified many specific loci with significant associations to psychiatric disorders. However, unless we can develop useful approaches for systematically turning genetic information into neurobiological insights about brain disorders, there is a danger that costly and hard-won genetic findings will not be exploitable to understand pathophysiology and generate important therapeutic hypotheses. The goal of our collaborative, interdisciplinary effort is to develop powerful, generalizable approaches for discovering how risk variants for psychiatric disorders shape neurobiological processes at multiple levels of analysis, and to identify the processes whose dysregulation underlies disease. To do this, we propose to develop new experimental and inferential systems to bridge a longstanding gap between human genetics and experimental biology. We aim to identify biological causes and effects that span the genetic, molecular, and cellular levels of the nervous system. Our interdisciplinary team will develop new experimental systems that measure genetic influences across levels of analysis (RNA, proteins, and cellular function including physiology) in precise, scalable, well- controlled ways. We will make use of emerging cellular systems including three-dimensional cortical spheroids and organoids, and radically novel ?population in a dish? experimental systems that collect data on cells from hundreds of donors in a shared environment, inferring donor identity at the time of phenotypic readout. The analysis of such systems in turn requires sophisticated inferential strategies and new ideas from computer science. We propose to develop and widely share experimental and computational resources, including cell lines, methods, datasets, and analytic tools. The successful completion of this work will identify key neurobiological processes for multiple psychiatric disorders, and fortify many other scientists in making such connections in their own work. We hope in so doing to create a new kind of interdisciplinary science that ? by combining the strengths of data-driven, unbiased human genetics with the power of emerging experimental systems ? transforms the rate at which human- genetic leads lead to insights about disease mechanisms.